During training, we set the upsampling factor \(M=16\) , and the boundary of \(\mathcal {D}\)  \(\delta =0.1\) . The ground-truth dense point clouds were obtained by sampling the corresponding mesh models through Poisson Disk Sampling [1]}.
For each shape, we randomly sampled \(N^{\prime }=2048\)  points on the surface, then added Gaussian noise with the standard deviation of 0.05, 0.02, or 0.03 to move these points away from the mesh to generate query points in each training iteration. The feature dimensions were \(d_1=384\)  and \(d_2=128\) .
We used the Adam [2]} optimizer to train our network and set the batch size to 16.
